Quantization of weights and activations is another common technique to reduce the model
size and computational cost of DNNs [1]}. Quantization expresses values (e.g., 32-bit floating point number)
at a lower-precision (e.g., 8-bit) integer and a small number of parameters.
Some training frameworks such as TensorFlow [2]} have introduced a
quantization-aware training mechanism to improve
accuracy [3]}. In addition, inference engines such as
TensorFlow Lite [4]}, PyTorch Mobile [5]}, and NVIDIA TensorRT [6]}
support 8-bit integer quantization. SoftNeuro also implements quantization-based
routines to accelerate convolutions, as well as other computations.
